Drilling parameter optimization method based on machine learning

A drilling parameter and machine learning technology, applied in the field of drilling, can solve problems such as optimization that is too theoretical, unable to use complex and diverse scenarios, etc., to achieve the effect of accurate data sets

Pending Publication Date: 2022-03-04
SOUTHWEST PETROLEUM UNIV
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Problems solved by technology

This technology is optimized from the perspective of the drilling rig, and it is not combined with the actual site conditions. Its optimization is too theoretical and it cannot be applied to complex and diverse scenarios.

Method used

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  • Drilling parameter optimization method based on machine learning
  • Drilling parameter optimization method based on machine learning
  • Drilling parameter optimization method based on machine learning

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Embodiment

[0083] Such as Figure 1 to Figure 16 As shown, the present embodiment provides a method for optimizing drilling parameters based on machine learning, which includes the following steps:

[0084] The first step is to collect the formation characteristic parameters of the area where the well is drilled, and preprocess the formation characteristic parameters. In this embodiment, the formation characteristic parameters include formation lithology parameters, shale content, compressive strength, shear strength, internal friction angle, internal friction force, rock hardness, drillability extreme value and rock abrasiveness parameters. In this embodiment, the preprocessing of the formation characteristic parameters includes data cleaning, discrete processing, normalization and data dimensionality reduction. Among them, the most commonly used method for data dimensionality reduction is Principal Component Analysis (PCA, Principal Component Analysis). Its goal is to map high-dimensi...

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Abstract

The invention discloses a drilling parameter optimization method based on machine learning, which comprises the following steps: acquiring stratum characteristic parameters of an area where a drilling well is located, and preprocessing the stratum characteristic parameters; stratigraphic feature clustering is carried out on the preprocessed stratigraphic feature parameters; and in combination with the clustered stratum characteristics and the drilling parameters, mechanical drilling speed prediction is carried out by using a BP neural network or a recurrent neural network, and a drilling parameter optimization model is constructed and obtained. By means of the scheme, the method has the advantages of being simple in logic, accurate, reliable and the like, and has high practical value and popularization value in the technical field of well drilling.

Description

technical field [0001] The invention relates to the technical field of drilling, in particular to a method for optimizing drilling parameters based on machine learning. Background technique [0002] The drilling process is a complex process affected by many parameters, and the ROP is affected by many factors in this process, and according to the nature of the factors, they can be divided into two categories: controllable factors and uncontrollable factors. Among them, controllable factors refer to factors that can be affected by human regulation, such as mechanical parameters (weight on bit, rotational speed, etc.), hydraulic parameters (displacement, standpipe pressure, etc.) and drilling fluid parameters (drilling fluid density, viscosity, etc.); Uncontrollable factors refer to objective factors that are not affected by human control, mainly including the lithological characteristics of the drilled strata. [0003] During the drilling process, the optimization of drilling...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06F30/27G06K9/62G06F119/02
CPCG06F30/27G06F2119/02G06F18/23G06F18/214
Inventor 付建红陈一凡彭炽白璟刘伟张超越董广建李兆丰
Owner SOUTHWEST PETROLEUM UNIV
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